With the advent of digital streaming technologies, as well as the development of ever-advanced network communications and data processing devices, television viewers are no longer content to simply channel-surf or browse on-demand content providers and hope to find a suitable and enjoyable movies or television shows. To the contrary, many viewers prefer to obtain audio/video content recommendations from various sources, including other people (e.g., friends, neighbors, coworkers) and television service providers (e.g., cable, satellite, set-top box (STB) providers), and to try out new shows and movies that have been recommended to them.
Television service providers generally provide recommendations by identifying content that similar people with similar tastes are selecting to watch. These recommendations are based on trends and historical viewing data, which is not available for new shows that have not previously been aired or made available via on-demand services. Thus, a user may not be aware of a new show, and the user will not receive a recommendation for the new show because it is not associated with historical viewing data.
Accordingly, it is desirable to provide recommendations based on other factors and/or variables, other than the viewing habits of other users. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background.